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Recent Dynamics of Returns to Education in Transition Countries
Tom Coup and Hanna Vakhitova*
Kyiv School of Economics and Kyiv Economics Institute
Abstract: This study provides recent estimates of returns to education in transition countries,
investigating how the economic boom in the region has affected these returns. We find that
transition countries continue to have relatively low returns to education and that the economic
boom did not lead to a clear change in these returns. A more detailed investigation for one
specific country, Ukraine, confirms these results.
Keywords: returns to education, transition countries
JEL Classifications: J24, J31, P2, P3, P5
*This paper is based on a report that was prepared as a background report for Ukraines Country
Economic Memorandum, financed by the World Bank. The findings, interpretations, and
conclusions expressed in this paper are entirely those of the authors. They do not necessarily
represent the views of the International Bank for Reconstruction and Development/World Bank
and its affiliated organizations, or those of the Executive Directors of the World Bank or the
governments they represent.
I. Introduction
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There is a large literature on the returns to education in transition countries and how these returns
have changed over time (see for example Flabbi et al, 2008 and Fleisher et al., 2005). The
literature so far, however, mainly consists of studies that use data from the 1990s and the early
2000s and very few results for transition countries are available for years later than 2002. In this
note, we provide more recent estimates of the returns to education covering the recent period of
economic growth in the transition countries (up to the 2008 financial crash). To the extent this
growth was skill-biased we would expect in an increase of returns to education over this period 1.
We provide a cross-country comparison of the recent dynamics of the returns to education using
the data from the 2007 wave of the International Social Survey Program (ISSP), which contains
individual information about earnings and education in about 30 countries23 In addition, we use a
more detailed data set to look at the dynamics in the returns to education in Ukraine, the
transition country which has consistently been found to have among the lowest returns toeducation.
The existing literature has found significant differences in returns to education across countries,
differences that have remained relatively constant over the last 15-20 years. In the early 2000s
the education wage premium in transition countries varied from 10-12 percent for China and
Hungary to 4.0 percent for Ukraine. While in most transition countries, the returns have showed
small increases over time, returns in Ukraine have barely moved. Table I in the Appendix gives
an overview of returns to education studies that focus on transition countries and the estimates
they found.
Our analysis of the recent ISSP data confirms many of the findings of the prior literature.
Following the basic specification in Flabbi et al. (2008), we first run a regression controlling for
potential experience and gender. Based on this regression, the estimated return to a year of
schooling in 2007 in transition countries varies between a low 5.2 percent in Ukraine to a high of
1In a similar vein, McGuinness et al, 2008, investigate how the economic boom in Ireland has changed returns to
education there.
2Flabbi, Paternostro, and Tiongson (2008) use earlier waves of the same survey program .
3 Ukraine was added to the list of ISSP countries in the 2008 wave and we use these 2008 data for Ukraine (the 2008ISSP data for other countries are not available yet). As the field work for the 2007 wave was done in the period2006-2008 and the field work for the Ukraine survey of the 2008 wave in 2008, we are able to provide comparisonsusing the same data source and methodology and similar time period.
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about 10 % in Poland (see Figure 1). Returns in non-transition countries are relatively low
compared to developing countries in the ISSP sample, and on average not unlike OECD
countries.
Figure 1 Returns to education by countries, 2007 wave basic specificationNote: Coefficients of the years of schooling variable in earning regressions. Dependentvariables are monthly earnings. Specification includes: potential experience (linear and
squared), dummy for gender. Source: Ukraine ISSP 2008, all other countries ISSP2007.
In figure 2 below we also report the results of a more extended specification which additionally
includes dummies for living in urban areas, marital status, controls for occupation particularities
(major occupation groups, public employee, working full-time, member of a trade union),
controls for current family (number of members, dummy for spouse working full-time). Addingthese controls reduces the estimated returns, with Latvia showing insignificant returns to
education and Slovakia and Slovenia having the highest returns at about 6 percent.
Figure 2 Returns to education by countries, 2007 wave extended specification
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Note: Coefficients of the years of schooling variable in earning regressions. Dependent
variables are monthly earnings. This specification includes more controls as explained in the
text. Source: Ukraine ISSP 2008, all other countries ISSP 2007.
Next we turn to table I which gives the dynamics of the returns to education using the basic
specification with a small number of controls.
Table 1 dynamics of returns, basic specification
Country /Basicspecification 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2007
Bulgaria 0.0470.05
20.05
3 0.050.04
90.07
2 0.052
Czech
0.036
0.044 0.07
0.076
0.054
0.087
0.066 0.069
Latvia
0.067
0.047
0.053
0.086
0.065 0.08
0.078 0.063
Poland 0.06 0.0710.08
1 0.080.07
9 0.070.06
50.08
10.07
90.09
20.10
6 0.098
Russia
0.028 0.038
0.043
0.054
0.083
0.065
0.072
0.084
0.083
0.084
0.074 0.082
Slovakia
0.061
0.066
0.059
0.061 0.073
Slovenia
0.063 0.058
0.088
0.095
0.117
0.099
0.089
0.081
0.082 0.083
Ukraine
2008 0.052
Note: Coefficients of the years of schooling variable in earning regressions with few controls
as specified in the text.
Source: Estimates for 1991-2002 are from Flabbi et al. (2008); estimates for 2007 and for
Ukraine are by the authors.
Based on this basic specification, we find that little has changed during the 2000s. We see a
decrease in returns in Bulgaria, Latvia and Poland, and an increase in the Czech Republic,
Russia, Slovakia and Slovenia. Both increases and decreases are small in size however.
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Table 2 gives the dynamics based on the extended specification. Returns in Bulgaria, Latvia,
Poland and also the Czech Republic show declines, while returns in Slovenia, Slovakia and
Russia increased4.
Table 2 dynamics of returns, extended specification
Country /Extendedspecification 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2007
Bulgaria 0.0340.03
60.03
50.03
60.02
80.05
7 0.032
Czech
0.028
0.033
0.048
0.066 0.03
0.063
0.034 0.033
Latvia
0.043
0.028 0.03 0.05
0.039
0.057
0.025 0.017
Poland
0.055 0.059
0.041
0.063
0.053
0.068
0.028
0.071 0.047
Russia 0.03 0.0140.04
1 0.06 0.030.04
60.06
80.05
60.04
1 0.056
Slovak
0.028
0.029 0.062
Slovenia
0.048 0.045
0.049
0.077
0.077
0.056
0.042
0.057 0.04 0.062
Ukraine
2008 0.024
Note: Coefficients of the years of schooling variable in earning regressions with many
controls as specified in the text. Source: Estimates for 1991-2002 are from Flabbi et al.
(2008); estimates for 2007 and for Ukraine are by the authors.
Overall, the dynamics in returns to education in the period 2002-2007 do not suggest that theeconomic boom that took place in that period in the countries under consideration affected
people with different amounts of education in different ways. Returns to education increased
slightly in some countries and decreased slightly in others, but overall returns to education
remained relatively moderate.
II. Focus on Ukraine
We next use a more comprehensive dataset on Ukraine to provide a more detailed view on the
recent dynamics of the returns to education. Previous papers which have estimated the returns to
education for Ukraine include Lechenko (2001), Herasym (2004) and Gorodnichenko and Peter
(2005)
4 Table 2a in the appendix has also a balanced specification which is the basic specificationbut only for those individuals for whom a complete set of data is available and are thusincluded in the sample used for the extended specification.
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Gorodnichenko and Peter (2005) provide a comprehensive study on the returns to education in
Ukraine using the retrospective part of the 2003 Ukrainian Longitudinal Monitoring Survey
(ULMS). Their estimates for 1997, 1998, 2000 and 2002 show a stable return of about 4% for
each year of education. In this study, we use the same set of control variables they suggest, to
provide estimates for 2003 and 2007, using the 2003 and 2007 waves of the ULMS. Because the
questions on education in 2007 were somewhat different from those in 2003, it is impossible
however to provide a perfect comparison (see discussion below)5.
We use, consistent with Gorodnichenko and Peter (2005, denoted as G&P hereafter), the
following variables: as dependent variable, we use the log of the monthly wage in the main job.
We use the last wage after December 2002 for 2003 and the last wage after December 2006
for 2007 the questionnaires were completed in April-May 2003 and throughout the year in
2007. As explicative variables we use
A dummy for gender (women take a value of 1)
A dummy for residence in the capital (residents of Kyiv take a value of 1)
dummies reflecting the size of the company, with the smallest companies with less than
10 employees being the benchmark
A dummy for privately owned companies and a dummy for foreign owned companies
Age and age squared, to capture differences in experience. Experience is typically
defined as age minus years of education minus 6. As we explain below, it is hard to find
good proxies for the total years of education, so we mainly use age rather than
experience.
Tenure and tenure squared reflecting the number of years in the firm
We also include dummies for the month during which the interview was taken, a dummy
for missing values of ownership status, and a dummy for missing values of firm size.
Our main explicative variable of interest, education, can be proxied in different ways. G & P
(2005) compute years of education based on the highest degree a respondent claims to have
obtained. In contrast to the ULMS 2003, the ULMS 2007 however does not have such question
and hence it is impossible to repeat the exact specification of G&P.
5Moreover, the ULMS 2003 was a random survey of the Ukrainian population while the ULMS 2007 followedpeople from the ULMS 2003, adding only new people if they belonged to the households that were included in the2003 survey.
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The ULMS 2003 and 2007, however, do have the following education questions in common
whether or not one has completed secondary education how many years one studied at vocational colleges (PTU) how many years one studied at professional colleges (Technicum) how many years one studied at academic institutions.
Together these questions give a reasonably complete picture of the educational track in Ukraine
and hence our first regression uses these 4 variables to compute the returns to education in
Ukraine and compare 2003 and 2007. Table 3 gives the results of regressing the logarithm of the
monthly salary on these 4 variables and the abovementioned control variables.
Table 3 Returns to education in Ukraine, 2003 and 2007
2003 2007(1) (2) (3) (4)
Secondary Degree 0.055**(2.00) 0.03(1.17) -0.002(-0.04) 0.013(0.48)Years of Vocational
Education0.026**(2.23)
0.01(1.05)
-0.014(-1.15)
0.001(0.12)
Years of ProfessionalEducation
0.044***(5.88)
0.022***(3.15)
-0.00(-0.01)
0.013**(2.06)
Years of AcademicEducation
0.058***(10.97)
0.031***(7.20)
0.056***(8.68)
0.049***(11.01)
Age 0.029***(4.61)
0.018***(3.16)
0.045***(7.56)
0.030***(6.75)
Age Squared -0.000***(-5.25)
-0.000***(-3.45)
-0.001***(-8.66)
-0.000***(-7.68)
Female -0.322***
(-14.44)
-0.272***
(-14.44)
-0.358***
(-14.71)
-0.328***
(-18.01)Capital 0.288***
(7.50)0.171***
(4.97)0.331***
(6.80)0.326***
(7.34)Tenure 0.007*
(1.77)-0.0000(-0.13)
0.011***(2.68)
0.007**(2.32)
Tenure Squared -0.000*(-1.82)
0.0000(0.15)
0.0000(-1.30)
0.0000(-1.09)
Private 0.173***(4.14)
0.113***(3.11)
0.200***(4.38)
0.188***(5.38)
Foreign Owned 0.205***(3.21)
0.104**(2.05)
0.135**(2.33)
0.062(1.14)
Firm with 10 to 50Employees
0.03(0.76)
-0.112***(-3.10)
-0.033(-0.88)
-0.084***(-2.72)
Firm with 50 to 100Employees
0.071*(1.68)
-0.111***(-2.78)
0.021(0.51)
-0.085**(-2.47)
Firm with 100 to 500Employees
0.137***(3.39)
-0.04(-1.12)
0.052(1.37)
-0.03(-0.97)
Firm with 500 to1000 Employees
0.267***(5.81)
-0.05(-1.02)
0.119**(2.18)
-0.019(-0.45)
Firm with more than1000 Employees
0.388***(9.40)
0.111***(2.92)
0.212***(4.28)
0.120***(3.45)
R2 Adjusted 0.17 0.15 0.154 0.208
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Number ofObservations
3558 2332 3179 2786
Dependent variable is the log of the monthly wage. Additional controls for the month during which the interviewwas taken, for missing values of ownership status, and for missing values of firm size. Columns (1) and (3) giveresults for the full sample. Columns (2) and (4) give results for sample of people earning more than the minimumwage. Robust standard errors are reported.
The first column shows that in 2003, having a secondary education increased ones monthly
wage by about 5.5%. Each year of education at a vocational school increased ones monthly
wage by 2.6%, compared to 4.4% for professional education and 5.8% for academic education.
In 2007 (column 3), however, only academic education had a significant return of 5.6% per
additional year. Other types of education had no effect on ones monthly wage. These numbers
confirm the stylized fact of relatively low returns to education in Ukraine and further suggest that
little has changed over time.
An explanation of why only academic education seems to be relevant in 2007 is provided by the
second and fourth column, which give the result of running a regression including only people
who earn more than the minimum wage (185 UAH in 2003, 420 UAH in 2007). While in the
2003 sample, only 2/3rd of the people in the sample were paid more than the official minimum
wage, in 2007 about 90% got paid more than the official minimum wage. If one restricts the
sample to those earning more than the minimum wage, the regressions in 2003 and 2007 show
similar results: no effect of secondary education and vocational education but significant effects
of professional and academic education. The returns to academic education are somewhat higherin 2007, the returns to professional education are somewhat lower. The fact that in this restricted
sample, secondary education and vocational education have no significant effect in 2003, suggest
that these types of education were helpful in 2003 in getting some wage but were not really
helpful in getting more than the minimum wage. As almost everybody earns more than the
minimum wage by 2007, these types of education have no longer an effect.
The control variables have the expected signs: women earn less on average (about 30%), while
residents of Kyiv earn more (20-30%). Older people and those having worked for a longer time
at the firm have higher wages. Finally, foreign owned and privately owned firms pay higher
salaries (10-20%), as do bigger firms.
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Using sample weights does not change our main findings. When we split up the sample by
gender, we get similar results and find that women have higher returns than men, a differential
that has increased somewhat over time.
As a second experiment, we use the ULMS 2003 question on the highest degree one obtained toestimate the returns to years of education for both 2003 and 2007, using only those people that
didnt receive a new degree after 2003 (i.e. they had already a higher education degree, and did
not get a new vocational, professional or academic degree). In this way, we use the same
questions for the same people, only in 2 different years and for a somewhat specific sample of
people who have at least secondary education and finished their education before 2003. We
transform the obtained degrees into adjusted years of education following the methodology
described in G&P, allocating a standard number of years of education to each degree.
Table 4 Returns to adjusted years of education, 2003 and 2007
2003 2007Full men women full men women
Adjusted Years ofEducation
0.057***(7.40)
0.054***(4.03)
0.059***(6.45)
0.061***(7.44)
0.045***(3.03)
0.074***(7.83)
Age 0.021**(2.28)
0.007(0.49)
0.033***(2.81)
0.049***(4.54)
0.053***(3.21)
0.044***(3.06)
Age Squared -0.000***(-2.79)
-0.0000(-0.88)
-0.000***(-3.07)
-0.001***(-5.55)
-0.001***(-4.05)
-0.001***(-3.60)
Female -0.274***(-8.38)
-0.340***(-9.68)
Capital 0.286***(4.98)
0.281***(2.87)
0.290***(3.91)
0.336***(5.18)
0.337***(3.01)
0.325***(4.14)
Tenure 0.005(0.75)
0.002(0.27)
0.006(0.74)
0.009*(1.80)
0.005(0.61)
0.007(1.22)
Tenure Squared 0.0000(-0.98)
0.0000(-0.54)
0.0000(-0.79)
0.0000(-0.58)
0.0000(0.11)
0.0000(-0.33)
Private 0.312***(5.44)
0.297***(3.41)
0.313***(4.07)
0.243***(4.13)
0.355***(4.28)
0.12(1.35)
Foreign Owned 0.176*(1.73)
0.045(0.28)
0.280**(2.40)
0.112(1.15)
0.077(0.45)
0.159(1.45)
Firm with 10 to 50
Employees
0.106**
(2.19)
0.017
(0.17)
0.162***
(3.00)
-0.059
(-1.18)
-0.156
(-1.60)
-0.003
(-0.06)Firm with 50 to 100Employees
0.150***(2.69)
-0.007(-0.06)
0.268***(4.46)
0.06(1.16)
-0.085(-0.79)
0.147***(2.70)
Firm with 100 to 500Employees
0.249***(4.56)
0.189*(1.89)
0.280***(4.14)
0.096*(1.89)
0.015(0.16)
0.152***(2.62)
Firm with 500 to1000 Employees
0.387***(6.22)
0.386***(3.61)
0.359***(4.76)
0.162**(2.06)
0.144(0.80)
0.165**(2.51)
Firm with more than1000 Employees
0.527***(9.08)
0.512***(4.97)
0.502***(7.42)
0.219***(3.06)
0.275**(2.21)
0.145*(1.70)
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R2 Adjusted 0.154 0.107 0.145 0.14 0.097 0.131Number of
Observations1778 745 1033 1857 795 1062
Dependent variable is the log of the monthly wage. Additional controls for the month during which the interviewwas taken, for missing values of ownership status, and for missing values of firm size. Adjusted years means yearsbased on the typical number of years it takes to reach a specific degree.
Again, we find relatively low returns to education at around 6% per year of (adjusted) education
and little change over time. Residents of Kyiv earn more, and foreign owned and bigger firms
pay higher salaries. Interestingly, women earn less on average (see the results of the full sample)
but their returns on education are higher, especially so in 2007.
Using these adjusted years of education (and our sample that is biased towards higher educated
people), we also checked whether the sector of employment influences the returns to education.
We distinguish between working in agriculture, industry, services and the social sector, with
agriculture being the base category. Note that, in terms of employment numbers in the sample,
between 2003 and 2007, we see a relative shift away from agricultural employment.
Table 5 Returns to education for specific sectors of employment, 2003 and 2007
2003 2007Industry Premium
1.268***
(3.19)
0.335***
(4.88)
Services Premium 1.450***(3.58)
0.234***(3.13)
Social Sector premium 0.581***(4.51)
0.127*(1.73)
Returns in the agricultural sector
0.108***
(3.54)
0.051***
(5.02)
Extra returns in industry
-0.064*
(-1.87)
0.012*
(1.95)
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Extra returns in services
-0.076**
(-2.24)
0.018***
(3.95)
Extra returns in the social sector
-0.03
(-0.94)
0.012**
(2.38)
Dependent variable is the log of the monthly wage. The controls mentioned above have also been included here butare not reported. Adjusted years of education are used here. The omitted category is the agricultural sector.
From table 8, we can see an interesting difference between 2003 and 2007. In 2003, wage was to
a large extent determined by the industry where one worked, with wages in the industry and
service sectors being more than double the wage in the agricultural sector. In 2007, the wage
effect of particular sectors has become much smaller. The returns to education also have become
less sector specific: while in 2003 returns to education where substantially bigger in those sectors
where the sector premium itself was low (agriculture and social), in 2007 the returns are fairly
similar across sectors, with the agricultural sector having somewhat lower returns and the
services sector somewhat higher returns.
The ULMS also allows us to investigate to what extent there is a skill mismatch between jobs
and education and how this affects returns to education. About two thirds of the respondents in
2003 and 2007 say their job corresponds to the level and field of education, while about 20% of
the respondents consider themselves overqualified (table 6).
Table 6 Job Requirements and Educational Level
2003 2007The job requires the same level of education and the same field 63.2 62.5The job requires a more advanced level of education 4.3 5.7The job requires a lower level of education 22.1 20.5The job requires the same level of education, but in a different discipline 10.5 11.3
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One possible explanation for the relatively low level of returns to education in Ukraine could be
this mismatch between education and jobs. Table 7 looks at this explicitly
Table 7 Returns and Skills Mismatch
2003 2007Premium for Under-Education 0.033
(0.79)-0.03
(-0.83)Premium for Over-Education 0.01
(0.52)-0.03
(-1.52)Premium for Right Level but
Wrong Field0.036*(1.68)
0.016(0.67)
Returns to Education if goodmatch
0.049***(5.15)
0.060***(5.24)
Extra Returns if Under-Education -0.134(-0.28)
0.432(0.99)
Extra Returns if Over-Education -0.29
(-1.23)
0.148
(0.63)Extra Returns for Right Level but
Wrong Field-0.409(-1.53)
-0.185(-0.63)
Dependent variable is the log of the monthly wage. The controls mentioned above have also been included here butare not reported. Adjusted years of education are used here. The omitted category is the good match category.
The data suggest however that this mismatch is not the main reason as the (subjective) quality of
the match has little or no influence on the level of the wage or on the returns to education.
Note that G&P (2005) find that the difference between Russia and Ukraine in terms of returns is
not due to supply factors. They conclude that The lower demand for educated labor, more
limited labor mobility, higher separation costs, and the larger extent of trade unions in Ukraine
are most likely determinants with a potential power to explain the differences in returns to
schooling.
III. Conclusions
In this paper, we provide recent estimates for the returns to education in transition countries,
adding estimates for the recent period of economic growth. Despite considerable economic
growth in the period 2003-2007, we do not find that education became relatively more valuable
over time throughout the region. Instead, we found small increases in some countries and small
decreases in other countries. Our more detailed results for Ukraine confirmed overall that the
economic growth did not have a major impact on the returns to education. The analysis for
Ukraine however does suggest that while in 2003 a secondary degree resulted in a somewhat
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higher wage, just having secondary education was no longer a differentiating factor in 2007, and
only academic education made a difference, possibly because of the fact that less and less people
were paid very small wages (i.e. less than the official minimum wage). In addition, sector
specific wage premia and differences in sector specific returns to education seem to have
declined.
Appendix
Table Ia - Returns to Education in Transition: A Summary of the Literature.
Country Estimates forentire country
Estimates bygender
Dataset Reference
Ukraine 2003 - 4.0% World Bank
2002 - 4.5%
2000 3.7%
1998 3.9%
1997 3.7%
1991 3.9%
1986 3.4%
ULMS Gorodnichenko andSabirianova Peter(2005)
Russia For men:
2000 6.8%
1998 5.1%
1996 5.2%
1994 5.0%
1992 3.4%
For women:
2000 7.6%
1998 7.7%
RLMS World Bank (2003)
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1996 5.6%
1994 7.4%
1992 3.8%
2002 9.2%6
2000 9.3%
1998 9.1%
1996 8.1%
1990 3.9%
1985 2.8%
RLMS Gorodnichenko andSabirianova Peter(2005)
2002 7.4%
2001- 8.4%
2000-8.3%
1999-8.4%
1997-7.2%
1996-6.5%
1995-8.3%
1994-5.4%
1993-4.3%
1992-3.8%
1991-2.8%
International SocialSurvey Programme(ISSP)
Flabbi, Paternostro,and Tiongson (2008)
2000 8.5%
1998 8.4%
1996 6.2%
For men:
2000 7%
1998 6.4%
RLMS 1992-2000 Vernon (2002)
6 Estimates differ for different studies by these authors.
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1994 7.3%
1992 5.2%
1996 4.7%
1994 5.5%
1992 5%
For women:
2000 10.3%
1998 10.2%
1996 7.7%
1994 9.1%
1992 5.4%
1996 7.2%
1995 7.5%
RLMS Psacharopoulos andPatrinos (2004) citedSabirianova andNesterova (1998)
For men:
1995 4.2%
For women:1995 5.6%
International SocialSurvey Programme(ISSP)
Harmon, Oosterbeek,and Walker (2003)
1998 11.8%
1996 6.6%
1995 6.2%
Clark (2003)
Czech Republic 2002 6.6% World Bank
2002 6.6%
2001 8.7%
1999 5.4%
1997 7.6%
1996 7.0%
International Social
Survey Programme(ISSP)
Flabbi, Paternostro,
and Tiongson (2008)
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1995 4.4%
1994 3.6%
1997 9.0%
1996 8.5%
1995 8.1%
Employer-basedsample of over
400,000 Czech and125,000 Slovak men
Filer, Juraida, andPlnovsk (1999)
For men:
2002- 11.1%
For women:
2002 8.9%
Information System onAverage Earnings(ISAE), quarterlynational employersurvey
Jurajda (2005)
For men:
1996 5.8%
1989 2.7%
Survey of 2,284 menfrom a stratifiedrandom sample ofhouseholds, 1989 and1996 cross-sectionaldata
Munich, Svejnar, andTerell (2005)
For women:
2002 6.8%
1996 7.1%
1989 3.7%
Survey of randomlyselected households in1996 and 2002
Munich, Svejnar, andTerell (2004)
For men:
1995 2.9%
For women:
1995 4.5%
International SocialSurvey Programme(ISSP)
Harmon, Oosterbeek,and Walker (2003)
For men:
1993 5.2%
For women:
Data from a multi-country
comparative researchproject entitled "SocialStratification in
Chase (1997)
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1993 5.8% Eastern Europe 1993"
Poland 2005 over
7% yearly7Households Budget
Survey (HBS)
Strawinski (2008)
2002 10.6%
2001 9.2%
1999 7.9%
1998 8.1%
1997 6.5%
1996 7.0%
1995 7.9%
1994 8.0%
1993 8.1%
1992 7.1%
1991 6.0%
International SocialSurvey Programme(ISSP)
Flabbi, Paternostro,and Tiongson (2008)
2000 10.6% World Bank
For men:
1995 7.4%
For women:
1995 10.3%
International SocialSurvey Programme(ISSP)
Harmon, Oosterbeek,and Walker (2003)
1996 7.3% For men:
1996 7.8%
For women:
Labor Force Survey /
LSMS type survey
Rutkowski (2001)
7 This is not an estimate from the Mincerian equation but from regression for secondary and tertiary educationseparately
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1996 6.7%
Romania 2003 4.2% World Bank
2000 8.5%
1999 8.2%
1998 7.8%
1997 6.9%
1996 6.7%
1995 6.7%
1995 5.9%
1990-93 6.4%
Integrated HouseholdSurvey (IHS) of theNational Institute ofStatistics
Andren, Earle andSapatoru (2005)
China 2005 12.1% For men:
2005 13.6%
For women:
2005 9.3%
Data on urban workersfrom the survey by theChinas Institute ofLabour Studies (ILS)
Qian and Smyth(2008)
For men:
Pooled data2004 and 2006 8.1%
For women:
Pooled data2004 and 2006 7.8%
China Health andNutrition Survey
(CHNS) conducted in2004 and 2006
Cnen and Hamori(2009)
2001 10.2%
2000 10.1%
Annual surveys ofurban
Zhang et al. (2005)
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1999 9.9%
1998 8.1%
1997 6.7%
1996 6.8%
1995 6.7%
1994 7.3%
1993 5.2%
1992 4.7%
1991 4.3%
1990 4.7%
1989 4.6%
1988 4.0%
households conductedby Chinas NationalBureau of Statisticsfrom 1988 through2001
2000 10.8%8 China UrbanHousehold Investmentand ExpenditureSurvey (CUHIES 2000)
Heckman and Li(2003)
1995 5.4% For men:
2005 4.3%
For women:
2005 6.9%
Second wave of theChinese
Household IncomeProject (CHIP-95)
Li (2002)
1993 12.2% 1993 Labor Survey bythe
Ministry of Labor
Psacharopoulos andPatrinos (2004) citedHossain (1997)
Hungary 2002 11.1% World Bank
8 Estimated from the average treatment effect of four year college attendance being 43% (the annual returnis 10.8%)
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2002 11.1%
2001 9.0%
1999 10.9%
1998 8.0%
1997 10.4%
1996 8.2%
1995 8.8%
1994 9.6%
1993 7.6%
1992 7.1%
1991 7.5%
1990 5.7%
1989 7.2%
1988 6.0%
1987 5.4%
1986 5.6%
International SocialSurvey Programme(ISSP)
Flabbi, Paternostro,and Tiongson (2008)
1998 11.2%
1995 11.2%
1992 10.0%
1989 8.5%
1986 6.4%
Wage and EarningsSurvey (WES) of theNational Labor Centerin Hungary
Campos and Jolliffe(2003)
For men:
1995 7.0%
For women:
1995 7.2%
International SocialSurvey Programme(ISSP)
Harmon, Oosterbeek,and Walker (2003)
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2000 10.4%
1992
9.4(4.0)%9
Varga and Galasi(2002)
Slovenia 2002 8.2%
2000 8.1%
1998 8.9%
1997 9.9%
1996 11.7%
1995 9.5%
1993 8.8%
1994 5.8%
1991 6.3%
International SocialSurvey Programme(ISSP)
Flabbi, Paternostro,and Tiongson (2008)
For men:
1995-8.9%
For women:
1995 11.2%
International SocialSurvey Programme(ISSP)
Harmon, Oosterbeek,and Walker (2003)
SlovakRepublic
2002 6.1% World Bank
2002 6.1%
1999 5.9%
1998 6.6%
1995 6.1%
International SocialSurvey Programme(ISSP)
Flabbi, Paternostro,and Tiongson (2008)
For men:
1995-5.0%
For women:
International SocialSurvey Programme(ISSP)
Harmon, Oosterbeek,and Walker (2003)
9 Basic and extended specifications (the latter in parenthesis).
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1995 6.4%
For men:
1993-4.9%
For women:
1993 5.4%
Data from a multi-country
comparative research
project entitled "SocialStratification inEastern Europe 1993"
Chase (1997)
Bulgaria 2003 6.7% World Bank
2002 7.2%
2000 4.9%
1999 5.0%
1997 5.3%
1993 5.2%
1992 4.7%
International SocialSurvey Programme(ISSP)
Flabbi, Paternostro,and Tiongson (2008)
For men:
1995-5.0%
For women:
1995 6.2%
International SocialSurvey Programme(ISSP)
Harmon, Oosterbeek,and Walker (2003)
2001 6.5% For men:
1995-6.2%
For women:
1995 6.8%
Labor Force Survey /LSMS type survey
Rutkowski (2001)
Latvia 2002 7.8% World Bank
2002 7.8% International SocialSurvey Programme(ISSP)
Flabbi, Paternostro,and Tiongson (2008)
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2001 8.0%
2000 6.5%
1999 8.6%
1998 5.3%
1996 4.7%
1995 6.7%
Kazakhstan 2001 8.1% World Bank
For men:
2001-8.0%
For women:
2001 11.5%
2001 KazakhstanHousehold
Budget Survey (KHBS)
Arabsheibani andMussurov (2007)
Moldova 2003 8.0% World Bank
Belarus 2002 6.9% World Bank
2001 10.7%
1996 10.1%10
1996 and 2001 waves of
the Belarusian Household
Survey of Incomes andExpenditure (BHSIE)
Pastore andVerashchagina (2006)
Tajikistan 2003 4.9% World Bank
Georgia 2006 6.2% For men:
2006-6.6%
For women:
2006 7%
Georgian Household
Survey
conducted between
1997 and 2006 years
Botchorishvili (2007)
2002 0.3% World Bank
10This is the annual rate of return to tertiary education. Estimates are obtained using the educationalqualifications, not the years of schooling.
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Croatia For men:
2004 6.2%
1996 4.9%
For women:
2004 7.8%
1996 5.1%
Labor Force Survey
(LFS) in 1996 and
2004
Vuji and oi(2009)
Table 2a. Return to a year of education in selected transition countries, 1994-2007
Country /Specification 1994 1995 1996 1997 1998 1999 2000 2001 2002 2007
Bulgaria
Basic 0.053 0.05 0.049 0.072 0.052
(0.010)
(0.010)
(0.009)
(0.011) (0.013)
BasicBalanced 0.055 0.05 0.05 0.074 0.053
(0.010)
(0.011)
(0.009)
(0.011) (0.013)
Richer 0.035 0.036 0.028 0.057 0.032
(
0.015)(0.
017)(
0.010)(
0.016)(0.019
)
Czech
Basic 0.036 0.044 0.07 0.076 0.054 0.087 0.066 0.069
(0.008)
(0.006)
(0.007)
(0.008)
(0.006)
(0.009)
(0.010)
(0.012)
BasicBalanced 0.042 0.042 0.07 0.082 0.051 0.086 0.062 0.069
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(0.007)
(0.006)
(0.008)
(0.008)
(0.006)
(0.009)
(0.010)
(0.012)
Richer 0.028 0.033 0.048 0.066 0.03 0.063 0.034 0.033
(0
.008)(
0.008)(
0.009)(
0.009)(0.
007)(
0.011)(
0.011)(0.012
)
Latvia
Basic 0.067 0.047 0.053 0.086 0.065 0.08 0.078 0.063
(0.010)
(0.008)
(0.01
0)(0.
010)(
0.009)(
0.009)(
0.011)(0.011
)
BasicBalanced 0.068 0.046 0.052 0.08 0.063 0.083 0.075 0.063
(0.010)
(0.008)
(0.01
0)(0.
010)(
0.010)(
0.009)(
0.012)(0.011
)
Richer 0.043 0.028 0.03 0.05 0.039 0.057 0.025 0.017
(
0.012)(
0.009)(0.01
3)(0.
014)(
0.015)(
0.010)(
0.013)(0.013
)
Poland
Basic 0.08 0.079 0.07 0.065 0.081 0.079 0.092 0.106 0.098
(0.008)
(0.008)
(0.009)
(0.009)
(0.01
0)(0.
010)(
0.010)(
0.011)(0.010
)
BasicBalanced 0.079 0.07 0.064 0.081 0.079 0.09 0.098
(0.008)
(0.009)
(0.009)
(0.01
0)(0.
010)(
0.010)(0.010
)
Richer 0.041 0.063 0.053 0.068 0.028 0.071 0.047
(
0.010)(
0.011)(
0.014)
(0.01
2)(0.
012)(
0.015)(0.016
)
Russia
Basic 0.054 0.083 0.065 0.072 0.084 0.083 0.084 0.074 0.082
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(0.008)
(0.011)
(0.010)
(0.011)
(0.012)
(0.011)
(0.011)
(0.013)
(0.009)
BasicBalanced 0.054 0.084 0.065 0.066 0.091 0.081 0.081 0.082
(0.009)
(0.011)
(0.011)
(0.011)
(0.013)
(0.011)
(0.011)
(0.009)
Richer 0.041 0.06 0.03 0.046 0.068 0.056 0.041 0.056
(0
.010)(
0.013)(
0.014)(
0.012)(0.
015)(
0.013)(
0.012)(0.010
)
Slovak
Basic 0.061 0.066 0.059 0.061 0.073
(0.007)
(0.00
7)(0.
006)(
0.007)(0.010
)
BasicBalanced 0.058 0.06 0.073
(0.006)
(0.007)
(0.010)
Richer 0.028 0.029 0.062
(0.
008)(
0.007)(0.010
)
Slovenia
Basic 0.095 0.117 0.099 0.089 0.081 0.082 0.083
(0.008)
(0.007)
(0.009)
(0.00
7)(
0.007)(
0.009)(0.009
)
BasicBalanced 0.098 0.113 0.093 0.089 0.082 0.075 0.083
(
0.008)
(
0.008)
(
0.009)
(0.00
7)
(
0.007)
(
0.009)
(0.009
)
Richer 0.077 0.077 0.056 0.042 0.057 0.04 0.062
(
0.012)(
0.011)(
0.012)
(0.01
0)(
0.009)(
0.009)(0.013
)
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Ukraine
2008
Basic 0.052
(0.011)
BasicBalanced 0.049
(0.009)
Richer 0.024
(0.011)
Note: Coefficients of the years of schooling variable in earning regressions. Dependent
variables are monthly earnings. Robust standard errors are in parenthesis. Basicspecification includes: potential experience (linear and squared), dummies for male. Richer
specification includes: previous regressors plus dummies for living in urban areas and
married, controls for current job (dummies for occupation, public employee, working fulltime, member of a trade union), controls for current family (number of members, dummy
for spouse working full time).
Source: Estimates for 1994-2002 are from Flabbi et al. (2008); estimates for 2007 and for
Ukraine are by authors.
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